Inventory Management Problem Research Articles

The Stability of MNL-Based Demand Under Dynamic Customer Substitution and Its Algorithmic Implications

We study the dynamic assortment planning problem under the widely utilized multinomial logit choice model (MNL). In this single-period assortment optimization and inventory management problem, the retailer jointly decides on an assortment, that is, a subset of products to be offered, as well as on the inventory levels of these products, aiming to maximize the expected revenue subject to a capacity constraint on the total number of units stocked. The demand process is formed by a stochastic stream of arriving customers, who dynamically substitute between products according to the MNL model. Although this dynamic setting is extensively studied, the best known approximation algorithm guarantees an expected revenue of at least 0.139 times the optimum, assuming that the demand distribution has an increasing failure rate. In this paper, we establish novel stochastic inequalities showing that, for any given inventory level, the expected demand of each offered product is “stable” under basic algorithmic operations, such as scaling the MNL preference weights and shifting inventory across comparable products. We exploit this sensitivity analysis to devise the first approximation scheme for dynamic assortment planning under the MNL model.

An Insight into the Impacts of Memory, Selling Price and Displayed Stock on a Retailer’s Decision in an Inventory Management Problem

The present paper aims to demonstrate the combined impact of memory, selling price, and exhibited stock on a retailer’s decision to maximizing the profit. Exhibited stock endorses demand and low selling prices are also helpful for creating demand. The proposed mathematical model considers demand as a linear function of selling price and displayed inventory. This work utilized fractional calculus to design a memory-based decision-making environment. Following the analytical theory, an algorithm was designed, and by using the Mathematica software, we produced the numerical optimization results. Firstly, the work shows that memory negatively influences the retailer’s goal of maximum profit, which is the most important consequence of the numerical result. Secondly, raising the selling price will maximize the profit though the selling price, and demand will be negatively correlated. Finally, compared to the selling price, the influence of the visible stock is slightly lessened. The theoretical and numerical results ultimately imply that there can be no shortage and memory restrictions, leading to the highest average profit. The recommended approach may be used in retailing scenarios for small start-up businesses when a warehouse is required for continuous supply, but a showroom is not a top concern.

Robust optimization for lot-sizing problems under yield uncertainty

Production yield can be highly volatile and uncertain, especially in industries where exogenous and environmental factors such as the climate or raw material quality can impact the manufacturing process. Thus, for production planning, it is necessary to take into account the production yield uncertainty to obtain robust and efficient plans. In this paper, we consider lot-sizing problems under yield uncertainty. We propose a multi-period, single-item lot-sizing problem with backorder and yield uncertainty via a robust optimization methodology. First, we formulate a robust model under a budgeted uncertainty set, which is optimized under the worst case perspective to ensure the feasibility of the proposed plan for any realization of the yield described by the uncertainty set. Second, we analyze the structure of the optimal lot-sizing solution, and we derive the optimal robust policy for the special case of the inventory management problem under a box uncertainty. These results help us develop a dynamic program with polynomial complexity for the lot-sizing problem with stationary yield rate. Finally, extensive computational experiments show the robustness and effectiveness of the proposed model through an average and worst case analyses. The results demonstrate that the robust approach immunizes the system against uncertainty. Moreover, a comparison of the robust model with the nominal model, the deterministic model with safety stock, and the stochastic model shows that the robust model balances the costs better by reducing the backorders at the expense of more often producing a larger amount of goods.

A Piecewise Objective Probabilistic Optimization Approach as Decision Making for Supplier Selection and Inventory Management With Price Discount

A mathematical model in the form of a piecewise objective probabilistic optimization approach was proposed in this study as the new decision-making tool to solve supplier selection and inventory management problems. The focus was on price discount and uncertain parameters such as product demand, product defect rate, and late-delivery product rate, which were approached using random variables with some known probability distribution function. Meanwhile, the decision variables contained in the model include the product volume ordered by each supplier at each time for each product type and those stored in the inventory to minimize the total operational cost in the problem. The corresponding optimization problem was solved using a probabilistic programming algorithm via the LINGO optimization tool. The computational simulation showed the proposed model provided the optimal decision, and this means it can be used as a decision-making tool by industrial practitioners.

Inventory Management Analysis to Support Sales of Jaket Keren SME Garut

The trend of society is undergoing a transition from offline to online. This also has an impact on the interest in shopping that utilizes online media. As a way to take advantage of this phenomenon, an SME by the name of Jaket Keren which sells leather fashion products has taken advantage of the opportunity by marketing their products on online media. However, Jaket Keren has problems with planning for inventory management operations, resulting in buildup and shortage of inventory. Therefore, this study aims to describe the problems of operational inventory management that occur in Economic Order Quantity, Inventory Cost and Safety Stock which are currently applied to Jaket Keren. The method used in this research is a qualitative method. The data collection method of this research uses in-depth interview and triangulation. In this study, four interviewees were selected consisting of owners, employees, tailors and resellers of the Jaket Keren. The results of research on the economic order quantity aspect at Jaket Keren are still frequent shortages or stockpiles of leather and synthetic jackets because Jaket Keren has limitations in the production process. In addition, Jaket Keren does not have a minimum quantity requirement for procurement of goods, and employee negligence often occurs which causes the availability of goods to be disrupted. This has an impact on Jaket Keren inventory cost, which places orders for other tailors that make use of costs for procurement of goods inefficiently, so that Jaket Keren will incur costs that are more expensive than when producing their own. Moreover, Jaket Keren does not yet have a minimum limit on the amount of inventory when placing an order, which causes Jaket Keren to not have a safety stock to anticipate possible purchase requests from their consumers.

Inventory allocation with full downward substitution and monotone cost differences

We study a single-period multi-product inventory allocation problem with full downward substitution and monotone cost differences. The cost structure with monotone differences is more general than the additive cost structure usually assumed in literature. Using the notion of Monge sequences, we identify conditions under which the problem can be solved efficiently using greedy allocation. For problems that do not meet these conditions, we develop an efficient algorithm that solves the problem to optimality. For this specific problem, our algorithm has substantially lower computational complexity than existing efficient algorithms for the more general transportation problem; we numerically confirm this superior computational efficiency and illustrate the importance of using efficient algorithms at the allocation stage of the inventory management problem.

Hybrid algorithm based on reinforcement learning for smart inventory management

This article proposes a hybrid algorithm based on reinforcement learning and the inventory management methodology called DDMRP (Demand Driven Material Requirement Planning) to determine the optimal time to buy a certain product, and how much quantity should be requested. For this, the inventory management problem is formulated as a Markov Decision Process where the environment with which the system interacts is designed from the concepts raised in the DDMRP methodology, and through the reinforcement learning algorithm—specifically, Q-Learning. The optimal policy is determined for making decisions about when and how much to buy. To determine the optimal policy, three approaches are proposed for the reward function: the first one is based on inventory levels; the second is an optimization function based on the distance of the inventory to its optimal level, and the third is a shaping function based on levels and distances to the optimal inventory. The results show that the proposed algorithm has promising results in scenarios with different characteristics, performing adequately in difficult case studies, with a diversity of situations such as scenarios with discontinuous or continuous demand, seasonal and non-seasonal behavior, and with high demand peaks, among others.

Dynamic pricing and inventory management in the presence of online reviews

We study the joint pricing and inventory management problem in the presence of online customer reviews. Customers who purchase the product may post reviews that would influence future customers' purchasing behaviors. We develop a stochastic joint pricing and inventory management model to characterize the optimal policy in the presence of online reviews. We show that a rating‐dependent base‐stock/list‐price policy is optimal. Interestingly, we can reduce the dynamic program that characterizes the optimal policy to one with a single‐dimensional state space (the aggregate net rating). The presence of online reviews gives rise to the trade‐off between generating current profits and inducing future demands, thus having several important implications for the firm's operations decisions. First, online reviews drive the firm to deliver a better service and attract more customers to post a review. Hence, the safety‐stock and base‐stock levels are higher in the presence of online reviews. Second, the evolution of the aggregate net rating process follows a mean‐reverting pattern: When the current rating is low (respectively, high), it has an increasing (respectively, decreasing) trend in expectation. Third, although myopic profit optimization leads to significant optimality losses in the presence of online reviews, balancing current profits, and near‐future demands suffices to exploit the network effect induced by online reviews. We propose a dynamic look‐ahead heuristic policy that leverages this idea well and achieves small optimality gaps that decay exponentially in the length of the look‐ahead time window. Finally, we develop a general paid‐review strategy, which provides monetary incentives for customers to leave reviews. This strategy facilitates the retailer to (partially) separately generate current profits and induce future demands via the network effect of online reviews.

Using Deep Learning to Improve Inventory Record Accuracy: Concept and Application

Maintaining accurate inventory records has been, and remains, a central problem for managing retail operations. Discrepancies between the physical and recorded stock lead to poor reordering decisions, the over- or understocking of products that increase waste or lost sales, respectively. In this study, we revisit this classic inventory management problem by investigating whether novel machine learning algorithms provide an improvement over established methods. Specifically, we explore the application of deep learning as a method to identify and correct impactful inventory record errors – those that may affect future reordering decisions – by leveraging product level, store level and inventory quality data. Our results show that a deep learning-based decision model outperforms traditional stock auditing strategies. We validate our model by running a field experiment on 450 large stores of a leading grocery retailer. The results demonstrate that a deep learning-based decision model can provide a significant improvement over traditional corrective strategies by refining the representation of the inventory record inaccuracy problem. More generally, our study highlights how generic algorithms, like deep learning, can be applied successfully to improve performance over existing specific methods, yet also, we identify the wider challenges that emerge when deploying those algorithms.

Consignment inventory management in a closed-loop supply chain for deteriorating items under a carbon cap-and-trade regulation

This paper studies an inventory management problem for a closed-loop supply chain that is operated under a consignment stock policy and subject to a cap-and-trade regulation. The supply chain makes both new products and remanufactured products, considering that new raw materials (NRMs), cores, and finished goods (FGs) suffer from deterioration. Mixed integer nonlinear programming models are established to jointly optimize the production sequence of new products and remanufactured products, number of shipments, shipment interval, batch size of FGs, and production cycle length to minimize the total of production, shipment, inventory, deterioration, and carbon emission costs per unit time. Exploring and using the structural properties of the models, a Taylor-series based method is designed to solve the models. Finally, numerical examples and sensitivity analysis are provided to show the effects of key factors on the operational decisions and on both the economy and environment. Results indicate that the deterioration of NRMs, cores, and FGs triggers the supply chain to run short production cycles while making more frequent shipments under the consignment stock policy. Both the economic and environmental costs increase with the deterioration rates. In addition, raising the carbon-trading price can reduce carbon emissions but increase the economic cost of the supply chain, rendering the urgency in reasonably determining carbon cap and trading price.

SUSTAINABLE VALUE CHAIN STRATEGY FOR TOFU SMEs

Globalization, free trade, and the current pandemic can be both a challenge and an opportunity for SMEs in Indonesia. One of the opportunities that arise from this phenomenon is the expansion of supply and demand networks. One of the SMEs in the food sector affected by the pandemic is the Tofu SME. Currently, only about 36% of tofu SMEs still survive in carrying out the production process. Most of the SMEs have problems in raw material inventory management, they do not yet have a standard warehouse. This problem minimizes their production capacity. Besides that, Tofu SMEs are still difficult to develop and sustain in the new normal era. For this reason, research on increasing the sustainability of SMEs needs to be carried out. The value chain analysis approach is used in the study to map the operational processes of SMEs and to identify activities and problems that make an important contribution to increasing the value of the SME business processes. K-means clustering method is used to group value chain levels. The Analytical Hierarchy Process (AHP) method is then used to choose a level-up strategy. Cluster 1 has the most members, which is 62 SMEs. Cluster 2 has the smallest member of 1 SME while cluster 3 has 9 SME members. The value coalition strategy is a recommended strategy to be applied to cluster 1 SME groups.

A study of an EOQ model where the demand depends on time and varying number of tourists using fuzzy triangular norms.

The fundamental prerequisite of decision making is how to aggregate individual expert’s preference information. For constructing various aggregation operators on intuitionistic fuzzy set, various kinds of t-norms and co-norms are the most significant tools. This work takes a closer look on various fuzzy triangular norms and uses them for the first time in an economic order quantity (EOQ) model. Traditional economic theory has a paradigm shift on objective factors affecting demand such as price and income. Recently, behavioural economics gives more weightage to psychological and social factors that affect our preferences and choices in markets differently. As per literature survey concern, this paper may indeed be the first to adopt the concept of the number of variations of a tourist’s dependent demand in inventory management problems. Utilizing several triangular norms, we have characterized Attanassov (standard) and non-Attanassov’s (non-standard) feasible region. A case study has been performed for numerical illustration and model validation. A solution algorithm, has also been developed which proves Attanassov’s solution space is quite inferior to non-Attanassov’s domain. Finally, sensitivity analysis and graphical illustrations are also provided to justify the model.Graphical abstract

A Data-Driven Approach to Multistage Stochastic Linear Optimization

We propose a new data-driven approach for addressing multistage stochastic linear optimization problems with unknown distributions. The approach consists of solving a robust optimization problem that is constructed from sample paths of the underlying stochastic process. We provide asymptotic bounds on the gap between the optimal costs of the robust optimization problem and the underlying stochastic problem as more sample paths are obtained, and we characterize cases in which this gap is equal to zero. To the best of our knowledge, this is the first sample path approach for multistage stochastic linear optimization that offers asymptotic optimality guarantees when uncertainty is arbitrarily correlated across time. Finally, we develop approximation algorithms for the proposed approach by extending techniques from the robust optimization literature and demonstrate their practical value through numerical experiments on stylized data-driven inventory management problems. This paper was accepted by David Simchi-Levi, optimization. Funding: S. Shtern was supported by the Israel Science Foundation [Grant 1460/19]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4352 .

A multi-stage stochastic inventory management model for transport companies including several different transport modes

ABSTRACT This paper deals with modeling and solving a multi-stage stochastic inventory management problem for transport companies, including several different transport modes. This model aims to determine the amount of stored cargo and transport containers and trucks to the appropriate locations. Due to the uncertainty of environmental conditions, the demand and transfer costs parameters are considered uncertainly in different scenarios in the proposed model. Since the uncertain model is insoluble, a new robust-fuzzy-probabilistic method has been used to control the model’s parameters. As a result, the final model minimizes the total cost of maintaining cargo and transporting trucks and containers. Three genetic algorithms, particle swarm optimization and gray wolf optimization have been employed to solve the proposed stochastic inventory management model. The results of the problem analysis show that the increase of uncertainty rate in different scenarios due to the increase in the amount of demand from cargo, transmission, and maintenance costs have increased. In return, the total network costs have also increased.

Application of fuzzy random-based multi-objective linear fractional programming to inventory management problem

This research article aims to study a multi-objective linear fractional programming (FMOLFP) problem having fuzzy random coefficients as well as fuzzy pseudorandom decision variables. Initially, the FMOLFP model is converted to a single objective fuzzy linear programming (FLP) model. Secondly, we show that a fuzzy random optimal solution of an FLP problem is resolved into a class of random optimal solution of relative pseudorandom linear programming (LP) model. As a result, some of theorems show that a fuzzy random optimal solution of a fuzzy pseudorandom LP problem is combined with a series of random optimal solutions of relative pseudorandom LP problems. As an application, the developed approach is implemented to an inventory management problem by taking the parameters as trapezoidal fuzzy numbers, ultimately resulting in a new initiative for modelling real-world problems for optimization. In the last, some numerical examples are introduced to clarify the obtained results and their applicability.

An integrated data-driven method using deep learning for a newsvendor problem with unobservable features

We consider a single-period inventory problem with random demand with both directly observable and unobservable features that impact the demand distribution. With the recent advances in data collection and analysis technologies, data-driven approaches to classical inventory management problems have gained traction. Specially, machine learning methods are increasingly being integrated into optimization problems. Although data-driven approaches have been developed for the newsvendor problem, they often consider learning from the available data and optimizing the system separate tasks to be performed in sequence. One of the setbacks of this approach is that in the learning phase, costly and cheap mistakes receive equal attention and, in the optimization phase, the optimizer is blind to the confidence of the learner in its estimates for different regions of the problem. To remedy this, we consider an integrated learning and optimization problem for optimizing a newsvendor’s strategy facing a complex correlated demand with additional information about the unobservable state of the system. We give an algorithm based on integrating optimization, neural networks and hidden Markov models and use numerical experiments to show the efficiency of our method. In an empirical experiment, the method outperforms the best competitor benchmark by more than 27%, on average, in terms of the system cost. We give further analyses of the performance of the method using a set of numerical experiments.

Determination the Role and Problems of Inventory Management and Supply chain Profitability: A case study of Cement companies in Sindh, Pakistan

Cement companies are private constrained are considered as one of the Pakistan’s biggest revenue produced. Lucky is one of the biggest cement company in Pakistan. In arrange to improve its client fulfillment it offers a wide extend of items in terms of Cement, and cement based items. Lucky Cement has significant intrigued towards the Green Supply Chain Administration. Nevertheless, it is one of the changeableness assignments to execute the green supply chain administration over the conventional supply chain but it gets to be the need of the advanced time and its usage is advantageous through each angle of trade. The reason of this inquire about is to examine and analyze the challenges for Green Supply Chain Administration in Lucky Cement private limited constrained and to supply proposals in this respect. The investigate discoveries are based on SWOT investigation and to uncover equality, shortcomings, openings and dangers with regard to challenges confronted by Lucky cement in executing Green Supply Chain Administration. A few convincible proposals is been given in this respect to overcome the challenges confronted by Lucky cement in actualizing or to receive the Green Supply Chain Administration. This investigate is of subjective in nature and burn case think about technique is utilized for point-by-point examination of Lucky cement as it were. Through non-probability, based inspecting strategy essential in hand information based on organized interviews was collected from concerns. The discoveries of the investigate concludes that the Lucky cement require execute Green Supply Chain Administration in its fabricating and crude fabric acquirement prepare and has sufficient potential to actualize it in arrange to improve efficiency and to manage with the client and the administrative weight. The company can contribute straightforwardly on nearby agriculturists by giving mindfulness so that they can make strides the abdicate of eatable oil seed edit, the company can too collaborate with their providers in arrange to center on switch coordination’s of their cement from clients for reusing reason or by getting skill of Green Supply Chain Administration experts.

СТРАТЕГІЧНЕ УПРАВЛІННЯ ЗАПАСАМИ ТОРГОВЕЛЬНОГО ПІДПРИЄМСТВА

In modern economic realities, the problem of strategic inventory management of a trade enterprise does not lose its relevance, because inventories are the main tool for ensuring business success, and at the same time they represent the main danger, since the share of goods inventories for an enterprise is about half of all its current assets. The article provides a critical analysis of the main types of inventory management strategies at the enterprise and proves the importance of the product sales management process in the strategic management system of a trading enterprise. Methods of stimulating the sale of goods and services are proposed, taking into account the specific features of the trade enterprises functioning. A scheme of strategic inventory management of a trading enterprise has been developed and strategic directions for inventory management have been proposed, which will help to solve problems in the enterprise's supply chains, reduce logistics costs, increase production efficiency, as well as ensure the competitiveness of goods, which will positively affect the final financial result of the enterprise.

Simulation model of the car service inventory management system: input parameters

The paper describes the problem of car service inventory management and the need for operational management of their quantity. The purpose and use of simulation modeling for solving the problem of order management are indicated. A model of inventory management at car service enterprises is constructed using simulation methods. The components of the simulation model are determined, which include controlled managed parameters, controlled unmanaged parameters, uncontrolled parameters and output indicators. For each component a characteristic is given. The input parameters of simulation modeling are presented, including the volumes of orders for car service repair parts. The input parameters used for the simulation are based on actual car dealership orders. Based on the data obtained, calculations of the arithmetic mean, standard deviation, median, coefficient of variation, asymmetry and kurtosis are performed. A distribution histogram is constructed for individual parameters. The results of the study reveal an exponential distribution of the volume of orders.

Моделювання управління запасами в бюджетному процесі промислового підприємства

The article is aimed at studying and substantiating the role of stocks in the budget process of an industrial enterprise, as well as elaborating a dynamic economic and mathematical model of inventory management, which would take into account the stochastic nature of demand for products and maximize profits that directly depend on the volume of output and, accordingly, on the stocks of finished products and raw materials available in stock. Since stocks affect profits, which, in turn, reflect a positive financial result of the enterprise, the application of the elaborated model will be the key to more effective activities of the economic entity as a whole. In addition, the use of the elaborated model simplifies the process of developing the enterprise’s budgets. The article proposes a perspective method for solving the problem of inventory management of an industrial enterprise in the budget process, taking into account the stochastic nature of demand. The simulation model of order formation and dynamic model of inventory management are elaborated, which allows to determine for each individual period the optimal production volumes that contribute to maximizing the total profit of the enterprise. The connection of stocks and budget is determined and the role of stocks in the budget process is established, as well as the need for research in this area. The elaborated model was applied at an industrial enterprise, which confirmed its effectiveness. Calculations for two different strategies for the purchase of raw materials are made and the optimal volumes of monthly production are determined. The result obtained through the use of the described methods is analyzed. The elaborated model can be used in the planning and development of budgets at any enterprise engaged in production activities. The actual direction of further research is the development and automation of the budgets consolidation system.